The combination of best available sources for lakes and wetlands on a global scale (1:1 to 1:3 million resolution), and the application of GIS functionality enabled the generation of a database which focuses in three coordinated levels on (1) large lakes and reservoirs, (2) smaller water bodies, and (3) wetlands. Level 1 (GLWD-1) comprises the 3067 largest lakes (area ≥ 50 km2) and 654 largest reservoirs (storage capacity ≥ 0.5 km3) worldwide, and includes extensive attribute data. Level 2 (GLWD-2) comprises permanent open water bodies with a surface area ≥ 0.1 km2 excluding the water bodies contained in GLWD-1. For GLWD-3, the polygons of GLWD-1 and GLWD-2 were combined with additional information on the maximum extents and types of wetlands. Class ‘lake’ in both GLWD-2 and GLWD-3 also includes man-made reservoirs, as only the largest reservoirs have been distinguished from natural lakes.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This is the Global Lakes and Wetlands (GLWD) database, version 2.0. The database development is fully described in Lehner et al. 2025 (see DOI-link in Related Materials below).There are 6 zipped data files (3 each in Geodatabase and in GeoTIFF format):"GLWD_v2_0_area_by_class_ha_gdb.zip" contains absolute wetland areas in hectares per grid cell for each individual wetland class, in Geodatabase format"GLWD_v2_0_area_by_class_pct_gdb.zip" contains wetland extents in percent coverage per grid cell for each individual wetland class, in Geodatabase format"GLWD_v2_0_combined_classes_gdb.zip" contains the combined wetland extents of all 33 classes (in ha and percent) as well as the dominant wetland type per grid cell, in Geodatabase format""GLWD_v2_0_area_by_class_ha_tif.zip" contains absolute wetland areas in hectares per grid cell for each individual wetland class, in GeoTIFF format"GLWD_v2_0_area_by_class_pct_tif.zip" contains wetland extents in percent coverage per grid cell for each individual wetland class, in GeoTIFF formatGLWD_v2_0_combined_classes_tif.zip" contains the combined wetland extents of all 33 classes (in ha and percent) as well as the dominant wetland type per grid cell, in GeoTIFF formatEach of the zip-files contains a Technical Documentation (GLWD_TechDoc_v2_0.pdf) which provides additional data descriptions. For more information on GLWD see https://www.hydrosheds.org/products/glwd.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This dataset estimates large-scale wetland distributions and important wetland complexes, including areas of marsh, fen, peatland, and water.

This data set estimates large-scale wetland distributions and important wetland complexes, including areas of marsh, fen, peatland, and water (Lehner and Döll 2004). Large rivers are also included as wetlands (lotic wetlands); it is assumed that only a river with adjacent wetlands (floodplain) is wide enough to appear as a polygon on the coarse-scale source maps. Wetlands are a crucial part of natural infrastructure as they help protect water quality, hold excess flood water, stabilize shoreline, and help recharge groundwater (Beeson and Doyle 1995, Stuart and Edwards 2006). Limited by sources, the data set refers to lakes as permanent still-water bodies (lentic water bodies) without direct connection to the sea, including saline lakes and lagoons as lakes, while excluding intermittent or ephemeral water bodies. Lakes that are manmade are explicitly classified as reservoirs. The Global Lakes and Wetlands Database combines best available sources for lakes and wetlands on a global scale. This data set includes information on large lakes (area ≥ 50 km2) and reservoirs (storage capacity ≥ 0.5 km3), permanent open water bodies (surface area ≥ 0.1 km2), and maximum extent and types of wetlands.

This map shows different wetland types, as well as lakes and rivers, across North America.The map was made using the Global Lakes and Wetlands Database (GLWD) Level 3, which was created using a variety of the best available sources for lakes and wetlands on a global scale.Level 3 of the Global Lakes and Wetlands Database (GLWD) comprises lakes, reservoirs, rivers, and different wetland types in the form of a global raster map at 30-sec resolution. GLWD-3 may serve as an estimate of wetland extents for global hydrology and climatology models, or to identify large-scale wetland distributions and important wetland complexes.Source: Lehner, B., and P. Döll. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296/1-4: 1–22. Global Lakes and Wetlands Database available through World Wildlife Fund (WWF).Files Download

HydroSHEDS provides hydrographic information in a consistent and comprehensive format for regional and global-scale applications. These data layers are available to support watershed analyses, hydrological modeling, and freshwater conservation planning at a quality, resolution, and extent that had previously been unachievable in many parts of the world.

Global Lakes and Wetlands Database Drawing upon a variety of existing maps, data and information, WWF and the Center for Environmental Systems Research, University of Kassel, Germany created the Global Lakes and Wetlands Database (GLWD). The combination of best available sources for lakes and wetlands on a global scale (1:1 to 1:3 million resolution), and the application of GIS functionality enabled the generation of a database which focuses in three coordinated levels on (1) large lakes and reservoirs, (2) smaller water bodies, and (3) wetlands.

To solve the high-frequency sample needs of time series wetland classification, we developed a method for automatically producing global wetland samples based on 13 global and regional wetland-related datasets and millions of images from Landsat 8 OLI, MODIS, Sentinel-1 SAR GRD, and Sentinel-2 MSI sensors. Considering the consistency of types and the separability of spectra, we summarized all classification systems into three types: wetland, water body, and non-wetland.Samples are randomly selected based on the equal-area stratified sampling scheme based on the existence probability of wetlands. In order to ensure sufficient samples, we proposed global sample size of 500,000. According to the global potential wetland distribution data set, the sample size of each grid was allocated, and samples were randomly selected. Based on 13 auxiliary data sets, we first determined the sample type according to the order of water body and wetland and assigned the "non-wetland" attribute to the type of neither water body nor wetland. The 13 auxiliary data sets include GlobeLand30 (Chen et al., 2014), FROM-GLC (Yu et al., 2013), GlobCover (Arino et al., 2010), GLC_FCS30_2020 (Liu et al., 2020), Joint Research Centre Global Surface Water Survey and Mapping map (Pekel et al., 2016), Global Reservoir and Dam Database (GRanD) (Lehner et al., 2011), Global Mangrove Watch (GMW) (Bunting et al., 2018), Global Lakes and Wetlands Database (GLWD) (Lehner et al., 2004), Murray Global Intertidal Change (MGIC) (Murray et al., 2019), CAS_Wetlands (Mao et al., 2020), CA_wetlands (Wulder et al., 2018), National Land Cover Database (NLCD) (Yang et al., 2018), Global Potential Wetland Distribution Dataset (GPWD) (Hu et al., 2017).We also included 139027 Landsat 8 OLI images, 21160 MOD09A1 images, 296479 Sentinel-1 SAR images, and 4553453 Sentinel-2 MSI images globally from January 1 to December 31, 2020. We extracted minimum, maximum, mean, and median information for each band and NDVI, NDWI, MNDWI, and LSWI indexes in four sensors of global wetland samples. In order to remove this part of the noise, this study kept the water, wetland, and non-wetland samples within one standard deviation of the annual mean of each spectral band as the sample's secondary screening conditions to ensure the accuracy of samples.The number of wetland samples determined by each sensor is different. Landsat 8 has a total of 202,111 samples, including 13,176 water bodies, 54,229 wetland samples, and 134,706 non-wetland samples; MODIS has a total of 190,898 samples, including 13,436 water body samples, 50,400 wetland samples, and 127,062 non-wetland samples ; Sentinel- has a total of 185,943 samples, including 10,885 water samples, 54,224 wetland samples, and 120,834 non-wetland samples; Sentinel-2 has a total of 185,484 samples, including 11,225 water samples, 52,142 wetland samples, and 122,117 non-wetland samples.They are stored separately in four shapefiles.

The map represents permanent water bodies at global scale (lakes and reservoirs), derived from a corrected version of the Global Lakes and Wetlands Database. Resolution is 30 arcseconds (approx. 1km). Natural water bodies (lakes) are indicated by value 1, Reservoirs are indicated by value 2. The map should be used to integrate the global flood hazard maps.

These data are high-resolution datasets related to in-land water for limnology (study of in-land waters) and remote sensing applications. This includes: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates on a high-resolution (1/360x1/360 degree) grid, produced by the Department of Meteorology at the University of Reading. Data was derived using the ESA CCI Land Cover Map (see linked documentation). Datasets containing information to locate and identify water bodies have been generated from high-resolution (1/360x1/360 degree, about 300mx300m) data locating static-water-bodies recently released by the Land Cover Climate Change Initiative (LC CCI) of the European Space Agency. The new datasets provide: distance to land, distance to water, water body identifiers and lake centre locations. The lake identifiers (IDs) are from the Global Lakes and Wetlands Database (GLWD), and lake centres are defined for in-land waters for which GLWD IDs were determined. The new datasets therefore link recent lake/reservoir/wetlands extent to the GLWD, together with a set of coordinates which locates unambiguously the water bodies in the database. The LC CCI water bodies dataset has been obtained from multi-temporal metrics based on time series of the backscattered intensity recorded by ASAR (Advanced Synthetic Aperture Radar) on Envisat between 2005 and 2010. Temporal change in water body extent is common. Future versions of the LC CCI dataset are planned to represent temporal variation, and this will permit these derived datasets to be updated. The paper associated with this dataset is: L.Carrea O. Embury C.J. Merchant "High-resolution datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates" Geoscience Data Journal, vol. 2 issue 2, pp. 83-97, November 2015. DOI: 10.1002/gdj3.32

Abundance of rivers and wetlands, by freshwater ecoregion.

The abundance of rivers and wetlands describes the degree to which a freshwater ecoregion is covered with these habitats. We calculated this abundance by combining selected classes of the Global Lakes and Wetlands Database (Level 3 of Lehner and Döll 2004), together with ESRI Rivers (2005) and the perennial rivers from ArcWorld 1:3million (ESRI 1992), all gridded to one-kilometer cell resolution. It should be noted that some ecoregions (e.g., Australia’s Arafura and Carpentaria Drainages and Great Diving Range on the northern and eastern coasts) are dominated by small rivers and streams that are not reflected properly in global river and wetland data sets; thus, abundance in these ecoregions may be underrepresented. We classified an ecoregion as “dominated by lakes and reservoirs” when it met thresholds set by regions of known lake and reservoir dominance versus river and wetland dominance. Known ecoregions were used to determine the algorithms to set the cutoffs between abundance classes.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

Data derived from:

ESRI 1992 and 2005. ESRI ArcWorld Database [CD] and ESRI Data & Maps [CD]. Redlands, CA: Environmental Systems Research Institute. Digital media.

Lehner, B., and P. Döll. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296: 1-22.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

For more about The Atlas of Global Conservation check out the web map (which includes links to download spatial data and view metadata) at http://maps.tnc.org/globalmaps.html. You can also read more detail about the Atlas at http://www.nature.org/science-in-action/leading-with-science/conservation-atlas.xml, or buy the book at http://www.ucpress.edu/book.php?isbn=9780520262560

Level 1 of the Global Lakes and Wetlands Database (GLWD) comprises the shoreline polygons of the largest lakes (area >= 50 square km) and reservoirs (storage capacity >= 0.5 cubic km) worldwide, including extensive attribute data.

This dataset provides global monthly wetland methane (CH4) emissions estimates at 0.5 by 0.5-degree resolution for the period 2001-2019 that were derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies that encompass the main sources of uncertainty in wetland CH4 emissions. There are 18 model configurations. WetCHARTs v1.3.1 is an updated product of WetCHARTs v1.0 Extended Ensemble. Three new features in the updated version include (1) the model output data is updated from 2001-2015 to 2001-2019, (2) the model drivers are replaced from using ERA-interim to ERA5 reanalysis data, and (3) the Global Lakes and Wetlands Database (GLWD) wetland extent definitions have been adjusted for the 50-100% Wetland, 25-50% Wetland, and Wetland Complex (0-25% Wetland) categories. The intended use of this product is as a process-informed wetland CH₄ emission data set for atmospheric chemistry and transport modeling. Users can compare estimates by model configuration to explore variability and sensitivity with respect to ensemble members

Scientific evidence indicates that global warming could lead to a sea-level rise (SLR) of 1 meter or more in the 21st century. In this research, we have assessed how that would affect coastal wetlands in 76 developing countries and territories, taking into account how much of wetlands would be submerged and how likely the wetlands would move inland as the coastline recedes. Geographic Information System (GIS) software has been used to overlay the best available, spatially-disaggregated global data on freshwater marsh, Global Lakes and Wetlands Database (GLWD) Coastal Wetlands and brackish/saline wetlands, with the inundation zones projected for 1m SLR. In order to assess the impact of SLR on wetlands and the potential for adaptation, the wetland migratory potential (WMP) characteristic in the Dynamic Interactive Vulnerability Assessment (DIVA) database from the DINAS-COAST project has been used (Vafeidis et al, 2008). Our research estimates the vulnerable freshwater marsh, swamp forest, GLWD Coastal Wetlands, and brackish/saline wetlands, areas at risk by country and territory.

Analysis of ‘WPS6277 - Wetlands at Risk from Sea-Level Rise’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://datacatalog.worldbank.org/search/dataset/0041078/ on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Scientific evidence indicates that global warming could lead to a sea-level rise (SLR) of 1 meter or more in the 21st century. In this research, we have assessed how that would affect coastal wetlands in 76 developing countries and territories, taking into account how much of wetlands would be submerged and how likely the wetlands would move inland as the coastline recedes.

Geographic Information System (GIS) software has been used to overlay the best available, spatially-disaggregated global data on freshwater marsh, Global Lakes and Wetlands Database (GLWD) Coastal Wetlands and brackish/saline wetlands, with the inundation zones projected for 1m SLR. In order to assess the impact of SLR on wetlands and the potential for adaptation, the wetland migratory potential (WMP) characteristic in the Dynamic Interactive Vulnerability Assessment (DIVA) database from the DINAS-COAST project has been used (Vafeidis et al, 2008). Our research estimates the vulnerable freshwater marsh, swamp forest, GLWD Coastal Wetlands, and brackish/saline wetlands, areas at risk by country and territory.

--- Original source retains full ownership of the source dataset ---

This data set consists of a subset of a 1-degree gridded global freshwater wetlands database (Stillwell-Soller et al. 1995). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10? N to 25? S, 30? to 85? W). The data are in ASCII GRID format.

The global freshwater wetlands database was assembled from two data sets: Aselman and Crutzen's (1989) wetlands data set and Klinger's political Alaska data set (pers. comm. to L. M. Stillwell-Soller, 1995). The aim of Stillwell-Soller's global data set was to provide an accurate, comprehensive and uniform set of files for convenient specification of wetlands in global climate models. The main source of data was Aselman and Crutzen's global maps of percent cover for a variety of wetlands categories at 2.5-degree latitude by 5-degree longitude resolution. There was some reorganization for seasonally varying categories. Aselman and Crutzen's data were interpolated to a standard 1-degree by 1-degree grid through bilinear interpolation. Their data were geographically complete except for the Alaskan region, for which Klinger's data set provided values.

More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/soller_wetlands/comp/soller_readme.pdf.

LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.

This data set shows on a 100-resolution the inundated areas for flood events with a return period of 100-years, based on GloFAS climatology. Permanent water bodies were derived from the Global Lakes and Wetlands Database and from the Natural Earth lakes map (naturalearthdata.com).

This layer is provided in the GloFAS interface as additional information for the user as it it provides a rough indication of where to expect inundations in case of flooding.

The data set consists of a southern Africa subset of the Global Distribution of Freshwater Wetlands database 1-degree data and are available in ASCII GRID and binary image files formats. The Global Distribution of Freshwater Wetlands database has been assembled from two data sets: Aselman and Crutzen's (AC) (1989) wetlands data set and Klinger's (pers. comm., 1995) Political Alaska data set. The aim is to provide an accurate, comprehensive and uniform set of files for convenient specification of wetlands in global climate models. The main source of data is AC global maps of percent cover for a variety of wetlands categories at 2.5-deg latitude by 5-deg longitude resolution. There is some reorganization for seasonally varying categories. Using bilinear interpolation, the AC data was interpolated to a standard 1-deg by 1-deg grid. The AC data set is geographically complete except for the Alaska region. More information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/soller_wetlands/c….

Monitoring water quality in lakes and reservoirs is key in maintaining safe water for drinking, bathing, fishing and agriculture and aquaculture activities. Long-term trends and short-term changes are indicators of environmental health and changes in the water catchment area. Directives such as the EU's Water Framework Directive or the US EPA Clean Water Act request information about the ecological status of all lakes larger than 50 ha. Satellite monitoring helps to systematically cover a large number of lakes and reservoirs, reducing needs for monitoring infrastructure (e.g. vessels) and efforts.

The Lake Water Quality products provide a semi-continuous observation record for a large number of medium and large-sized lakes, according to the Global Lakes and Wetlands Database (GLWD) or otherwise of specific environmental monitoring interest.

They consist of three water quality parameters: (i) the turbidity of a lake, that describes water clarity or whether sunlight can penetrate deeper parts of the lake. Turbidity often varies seasonally, both with the discharge of rivers and growth of phytoplankton; (ii) the trophic state index that is an indicator of the productivity of a lake in terms of phytoplankton, and indirectly, over longer time scales, reflects the eutrophication status of a water body and (iii) the lake surface reflectances that describe the apparent colour of the water body and are intended for scientific users interested in further development of algorithms. The visual reflectance bands can also be combined into true-colour images.